This collection presents new insights on climate variability and advanced data analysis methods, highlighting machine learning's role.
Climate variability affects multiple processes on Earth, with significant system effects driving hydrometeorological, glaciological, atmospheric, and geophysical variability. Research into these fields is driven by acquisition and processing of voluminous amount of data at multiple spatial and temporal scales. Intersection of data and tools to work around this complexity, to extract a consistent and useful picture of the effects of climate change in the Earth System, requires handling of big data sets and their processing tools. This effort is generating novel approaches to the analysis of big data sets and new perspective on the predictive power of the tools used. For this reason, in March 2023, AGU launched the Special Collection Analyzing Big Data for Understanding Climate Variability , Natural Phenomena , and Rapid Environmental Changes , inviting contributions to showcase the latest advances and the role of machine learning and deep learning in climate data analysis. In this introduction, we outline the key findings and insights presented in 16 articles published in the special collection, and we highlight the emerging trends within this field of research. The following journals participated in the special collection: Journal of Geophysical Research : Solid Earth , Journal of Geophysical Research : Atmospheres , Geophysical Research Letters , and Earth and Space Science .
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Montillet et al. (2025) studied this question.
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